--------------------------------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\mvl\Downloads\dataverse_files\survey_results.log
  log type:  text
 opened on:  24 Jan 2017, 08:50:59

. 
. 
. 
. *generating temporary files
. tempfile temp1

. tempfile temp2

. tempfile temp3

. tempfile temp4

. tempfile temp5

. 
. *************************************
. *ANALYSIS PART 0: Index construction*
. *************************************
. 
. * looking at reliability of the two questions comprising the variable "integration"
. alpha int_leaveorstay int_advantage

Test scale = mean(unstandardized items)
Reversed item:  int_leaveorstay

Average interitem covariance:     .7222962
Number of items in the scale:            2
Scale reliability coefficient:      0.7700

. 
. *analyzing straight lining - 14 resp doing this
. ta dk_sl

   Straight |
 lining for |
 Don't Know |
    Answers |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      2,296       99.39       99.39
          2 |         14        0.61      100.00
------------+-----------------------------------
      Total |      2,310      100.00

. 
. ************************
. *ANALYSIS PART 1: H1-H3*
. ************************
. 
. * Setting 12 weeks out as baseline
. fvset base 12 wave

. 
. **Figure 3/Row 1 of table 1
. eststo b: reg int_eu int_dk i.wave i.ny_udd i.parlvote c.age i.male , r

Linear regression                                      Number of obs =    2310
                                                       F( 29,  2280) =  146.26
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.5390
                                                       Root MSE      =  17.792

------------------------------------------------------------------------------
             |               Robust
      int_eu |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      int_dk |   .7763862   .0152595    50.88   0.000     .7464623    .8063101
             |
        wave |
          0  |   2.876469   1.380297     2.08   0.037     .1696998    5.583239
          2  |  -.1360731   1.390563    -0.10   0.922    -2.862974    2.590828
          4  |  -.9409914    1.48843    -0.63   0.527     -3.85981    1.977827
          6  |   1.801377   1.335908     1.35   0.178    -.8183447    4.421099
          8  |   .2221066    1.37481     0.16   0.872    -2.473902    2.918115
         10  |   .7518794   1.516022     0.50   0.620    -2.221048    3.724806
             |
      ny_udd |
   ALMENGYM  |   .5943156   1.847129     0.32   0.748    -3.027913    4.216544
   ERHVERVS  |   1.561321   2.234244     0.70   0.485    -2.820042    5.942684
   ERHVERVS  |  -.8854235   1.437862    -0.62   0.538    -3.705078    1.934231
   KORTE VI  |   .8700264   1.611634     0.54   0.589    -2.290396    4.030449
   MELLEMLA  |  -.4157407   1.351426    -0.31   0.758    -3.065894    2.234413
   LANGE VI  |   2.413202    1.50284     1.61   0.108    -.5338745    5.360279
             |
    parlvote |
   B: Radik  |   .5389346   1.430996     0.38   0.706    -2.267256    3.345125
   C: Konse  |   -.966046   1.600631    -0.60   0.546    -4.104891    2.172799
   F: Socia  |  -3.596512   1.615492    -2.23   0.026      -6.7645   -.4285251
   I: Liber  |  -2.290598   1.893868    -1.21   0.227    -6.004482    1.423286
   K: Krist  |  -2.985303   6.128349    -0.49   0.626    -15.00303     9.03242
   O: Dansk  |    .647883   1.598808     0.41   0.685    -2.487388    3.783154
   V: Venst  |  -1.229398   1.119066    -1.10   0.272    -3.423892    .9650971
   �: Enhed  |  -1.452563   1.472812    -0.99   0.324    -4.340755    1.435629
   Andre pa  |    9.96201   2.333732     4.27   0.000      5.38555    14.53847
   Stemte b  |   3.528635    2.23845     1.58   0.115    -.8609769    7.918246
   Stemte i  |  -1.994486   3.524965    -0.57   0.572    -8.906959    4.917988
   Havde ik  |   5.956466   2.347075     2.54   0.011      1.35384    10.55909
   Vil ikke  |   1.731657   2.189902     0.79   0.429    -2.562751    6.026065
   Ved ikke  |  -.9636799   2.718392    -0.35   0.723     -6.29446      4.3671
             |
         age |   .0968526    .029953     3.23   0.001     .0381146    .1555907
      1.male |   1.054581   .8036761     1.31   0.190    -.5214322    2.630593
       _cons |  -.7067783   2.295634    -0.31   0.758    -5.208528    3.794972
------------------------------------------------------------------------------

. margins, over(wave) 

Predictive margins                                Number of obs   =       2310
Model VCE    : Robust

Expression   : Linear prediction, predict()
over         : wave

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        wave |
          0  |   66.43082    .932418    71.25   0.000     64.60234    68.25929
          2  |   63.50309    .940456    67.52   0.000     61.65885    65.34733
          4  |   63.49206   1.079866    58.80   0.000     61.37444    65.60969
          6  |   63.72283    .874171    72.90   0.000     62.00857    65.43708
          8  |   63.02228   .9339877    67.48   0.000     61.19073    64.85384
         10  |   62.79461   1.105164    56.82   0.000     60.62738    64.96185
         12  |   62.15805   1.013372    61.34   0.000     60.17083    64.14528
------------------------------------------------------------------------------

. marginsplot, xscale(reverse) ymtick(60(1)70) ylabel(60(2)70, labsize(large)) level(90) ///
>  graphregion(col(white)) scheme(s2mono) ytitle("Interest in EU (0-100)" " ", size(large) ) ///
>  xtitle(" " "Weeks before the election", size(large)) ///
> title(" ") saving(`temp1')

  Variables that uniquely identify margins: wave
(file C:\Users\mvl\AppData\Local\Temp\ST_16000001.tmp saved)

. 
. *Figure 4/Row 2 of table 1*
. eststo c: reg dk i.wave i.ny_udd i.parlvote c.age i.male, r

Linear regression                                      Number of obs =    2310
                                                       F( 28,  2281) =    8.96
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1307
                                                       Root MSE      =  17.618

------------------------------------------------------------------------------
             |               Robust
          dk |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        wave |
          0  |   4.909203   1.279158     3.84   0.000     2.400768    7.417638
          2  |   2.877403   1.406049     2.05   0.041     .1201348     5.63467
          4  |   1.517105   1.391821     1.09   0.276    -1.212263    4.246472
          6  |   .8423817   1.378906     0.61   0.541    -1.861659    3.546423
          8  |   2.167713   1.373492     1.58   0.115    -.5257102    4.861137
         10  |  -1.536326   1.509457    -1.02   0.309    -4.496378    1.423727
             |
      ny_udd |
   ALMENGYM  |   .2561452   2.107088     0.12   0.903    -3.875864    4.388154
   ERHVERVS  |   2.718224     2.4279     1.12   0.263      -2.0429    7.479347
   ERHVERVS  |   .9636812   1.489933     0.65   0.518    -1.958085    3.885447
   KORTE VI  |   3.897937   1.631387     2.39   0.017     .6987798    7.097094
   MELLEMLA  |   2.791678   1.369958     2.04   0.042     .1051842    5.478172
   LANGE VI  |   5.083595   1.458378     3.49   0.000     2.223709    7.943481
             |
    parlvote |
   B: Radik  |  -.5074686   1.508859    -0.34   0.737    -3.466348    2.451411
   C: Konse  |   1.464491    1.35608     1.08   0.280    -1.194789     4.12377
   F: Socia  |  -1.455463   1.532493    -0.95   0.342    -4.460689    1.549762
   I: Liber  |   .4895548   1.871336     0.26   0.794    -3.180143    4.159252
   K: Krist  |  -18.09014    8.66206    -2.09   0.037    -35.07647   -1.103796
   O: Dansk  |   1.695226   1.337844     1.27   0.205    -.9282923    4.318745
   V: Venst  |   .5976773   .9988609     0.60   0.550    -1.361093    2.556448
   �: Enhed  |  -.5137069   1.446554    -0.36   0.723    -3.350406    2.322992
   Andre pa  |  -1.462323   5.516277    -0.27   0.791    -12.27977    9.355121
   Stemte b  |  -2.510648   3.211292    -0.78   0.434    -8.808006     3.78671
   Stemte i  |  -20.53127   5.370593    -3.82   0.000    -31.06302   -9.999509
   Havde ik  |   .3905887   4.069132     0.10   0.924    -7.588997    8.370175
   Vil ikke  |  -7.415176   2.880579    -2.57   0.010      -13.064   -1.766347
   Ved ikke  |   -25.5855   7.023018    -3.64   0.000    -39.35767   -11.81333
             |
         age |   .1461887   .0287844     5.08   0.000     .0897423    .2026351
      1.male |   6.889277    .735665     9.36   0.000     5.446634    8.331919
       _cons |   76.48736   2.411559    31.72   0.000     71.75828    81.21644
------------------------------------------------------------------------------

. margins, over(wave)

Predictive margins                                Number of obs   =       2310
Model VCE    : Robust

Expression   : Linear prediction, predict()
over         : wave

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        wave |
          0  |   94.15094   .7831229   120.22   0.000     92.61524    95.68665
          2  |   91.79012   .9787167    93.79   0.000     89.87086    93.70939
          4  |   90.98413    .953887    95.38   0.000     89.11355     92.8547
          6  |    89.8913   .9566371    93.97   0.000     88.01533    91.76727
          8  |    90.8078   .9381859    96.79   0.000     88.96801    92.64759
         10  |   86.93603   1.161051    74.88   0.000      84.6592    89.21285
         12  |   88.81459   .9982342    88.97   0.000     86.85705    90.77213
------------------------------------------------------------------------------

. marginsplot, xscale(reverse) ymtick(82(1.5)98) ylabel(82(3)98, labsize(large)) level(90) ///
>  graphregion(col(white)) scheme(s2mono) ytitle("Average percent informed" " ", size(large)) ///
>  xtitle(" " "Weeks before the election", size(large)) ///
>  title(" " " ", size(large)) saving(`temp2')

  Variables that uniquely identify margins: wave
(file C:\Users\mvl\AppData\Local\Temp\ST_16000002.tmp saved)

. 
. *Figure 5/Row 3 of table 1*
. eststo a: reg patent_cor i.wave i.ny_udd i.parlvote c.age i.male, r

Linear regression                                      Number of obs =    1706
                                                       F( 28,  1677) =   10.92
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1490
                                                       Root MSE      =  33.692

------------------------------------------------------------------------------
             |               Robust
  patent_cor |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        wave |
          0  |   12.36765   2.935096     4.21   0.000     6.610811    18.12449
          2  |   5.767617   3.182883     1.81   0.070    -.4752257    12.01046
          4  |   6.526881     3.0617     2.13   0.033     .5217254    12.53204
          6  |   1.200751   3.059661     0.39   0.695    -4.800407    7.201909
          8  |   3.724815   2.991634     1.25   0.213    -2.142916    9.592545
         10  |  -1.236896    3.23682    -0.38   0.702    -7.585529    5.111736
             |
      ny_udd |
   ALMENGYM  |   10.49682   4.500876     2.33   0.020     1.668891    19.32474
   ERHVERVS  |   14.70678   5.016725     2.93   0.003     4.867074    24.54648
   ERHVERVS  |   7.294767   3.216493     2.27   0.023     .9860035    13.60353
   KORTE VI  |    11.2952   3.588511     3.15   0.002     4.256763    18.33363
   MELLEMLA  |    14.2854   3.030532     4.71   0.000     8.341372    20.22942
   LANGE VI  |   18.29144   3.334568     5.49   0.000     11.75108    24.83179
             |
    parlvote |
   B: Radik  |   6.516969   3.130244     2.08   0.037     .3773724    12.65657
   C: Konse  |  -.4568089   4.242252    -0.11   0.914    -8.777475    7.863858
   F: Socia  |   2.794799   3.839187     0.73   0.467    -4.735304     10.3249
   I: Liber  |   4.438758   4.360284     1.02   0.309    -4.113415    12.99093
   K: Krist  |  -20.76372   12.46757    -1.67   0.096    -45.21736     3.68991
   O: Dansk  |  -5.037262    2.92845    -1.72   0.086    -10.78106    .7065404
   V: Venst  |   -2.09392    2.12311    -0.99   0.324    -6.258144    2.070303
   �: Enhed  |  -7.392851   4.399171    -1.68   0.093    -16.02129    1.235592
   Andre pa  |  -10.84532   16.31514    -0.66   0.506     -42.8455    21.15485
   Stemte b  |  -18.96973   7.465906    -2.54   0.011     -33.6132   -4.326252
   Stemte i  |  -34.65559   8.379463    -4.14   0.000     -51.0909   -18.22029
   Havde ik  |  -11.14608   7.468227    -1.49   0.136     -25.7941    3.501952
   Vil ikke  |   -21.6802   5.460762    -3.97   0.000    -32.39083   -10.96957
   Ved ikke  |  -30.68899   9.339382    -3.29   0.001    -49.00707   -12.37092
             |
         age |   .2580254   .0643284     4.01   0.000     .1318529    .3841979
      1.male |   14.07196   1.691404     8.32   0.000     10.75448    17.38945
       _cons |   44.40148   5.235277     8.48   0.000     34.13312    54.66985
------------------------------------------------------------------------------

. margins, over(wave)

Predictive margins                                Number of obs   =       1706
Model VCE    : Robust

Expression   : Linear prediction, predict()
over         : wave

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        wave |
          0  |   84.71545    1.94659    43.52   0.000     80.89744    88.53345
          2  |   77.61905   2.289279    33.91   0.000      73.1289    82.10919
          4  |    79.0436   2.125295    37.19   0.000     74.87509    83.21211
          6  |   72.63158   2.103498    34.53   0.000     68.50582    76.75734
          8  |         75   2.012363    37.27   0.000     71.05299    78.94701
         10  |    69.7479   2.340955    29.79   0.000      65.1564     74.3394
         12  |   72.45817   2.194534    33.02   0.000     68.15386    76.76249
------------------------------------------------------------------------------

. marginsplot, xscale(reverse) ylabel(60(5)90, labsize(large)) ymtick(60(2.5)90) level(90) ///
>  graphregion(col(white)) scheme(s2mono) xlabel(,labsize(large)) ytitle("Average percent correct" " ", size(large) ) ///
>  xtitle(" " "Weeks before the election", size(large)) ///
>  title(" ") saving(`temp3')

  Variables that uniquely identify margins: wave
(file C:\Users\mvl\AppData\Local\Temp\ST_16000003.tmp saved)

. 
. 
. 
. **Exporting table 1 
. esttab b c a  using tabel1.rtf, replace stats(N r2 rmse, label("Observation" "R squared" "RMSE") fmt(%9.0f %9.2f %9.2f) ) ///
> keep(1.male age 10.wave 8.wave 6.wave 4.wave 0.wave 2.wave 2.ny_udd 3.ny_udd 4.ny_udd 5.ny_udd 6.ny_udd 7.ny_udd) label ///
> varlabel(1.male "Male (ref: Female)" age "Age (years)" 10.wave "10 weeks out (ref: election week)" ///
> 8.wave "8 weeks out" 6.wave "6 weeks out" 4.wave "4 weeks out" 2.wave "2 weeks out" ///
> 2.ny_udd "High School (ref: Primary school)" 3.ny_udd "Vocational high school" 4.ny_udd "Vocational school" ///
> 5.ny_udd "Shorter tertiary education" 6.ny_udd "Tertiary education" 7.ny_udd "Longer tertiary education") ///
> star("+" 0.1 "*" 0.05) se b(%9.2f) varwidth(40) title("Table 1. Campaign effects on interest, information and knowledge")
(note: file tabel1.rtf not found)
(output written to tabel1.rtf)

. 
. **Exporting figures 4-6
. graph use `temp1'

. graph export figur3.eps, replace
(note: file figur3.eps not found)
(file figur3.eps written in EPS format)

. graph use `temp2'

. graph export figur4.eps, replace
(note: file figur4.eps not found)
(file figur4.eps written in EPS format)

. graph use `temp3'

. graph export figur5.eps, replace
(note: file figur5.eps not found)
(file figur5.eps written in EPS format)

. 
. 
. *********************
. *ANALYSIS PART 2: H4*
. *********************
. 
. *changing wave variable to foster easier intepretation
. replace wave=12-wave
(1942 real changes made)

. 
. *saving estimates for table 2
. eststo a1: logit gov c.wave##(c.eval) c.econ c.integration c.ideology##c.ideology i.ny_udd i.lgov i.lparty_pro c.age i.male  

Iteration 0:   log likelihood = -869.79879  
Iteration 1:   log likelihood = -448.54347  
Iteration 2:   log likelihood = -384.22642  
Iteration 3:   log likelihood = -379.01583  
Iteration 4:   log likelihood = -378.97536  
Iteration 5:   log likelihood = -378.97534  

Logistic regression                               Number of obs   =       1469
                                                  LR chi2(17)     =     981.65
                                                  Prob > chi2     =     0.0000
Log likelihood = -378.97534                       Pseudo R2       =     0.5643

---------------------------------------------------------------------------------------
                  gov |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
                 wave |   .1312654   .0595413     2.20   0.027     .0145667    .2479642
                 eval |   .5168935   .0780861     6.62   0.000     .3638476    .6699394
                      |
        c.wave#c.eval |  -.0309296   .0100346    -3.08   0.002     -.050597   -.0112623
                      |
                 econ |   .1684713   .5151752     0.33   0.744    -.8412535    1.178196
          integration |   .3837783    .047504     8.08   0.000     .2906722    .4768844
             ideology |   .5113537    .115055     4.44   0.000       .28585    .7368573
                      |
c.ideology#c.ideology |  -.0978051   .0164821    -5.93   0.000    -.1301094   -.0655009
                      |
               ny_udd |
            ALMENGYM  |   1.234365   .5028599     2.45   0.014     .2487778    2.219952
            ERHVERVS  |   .2246049   .6054933     0.37   0.711    -.9621402     1.41135
            ERHVERVS  |   .3894889    .385262     1.01   0.312    -.3656108    1.144589
            KORTE VI  |   .1458098   .4534454     0.32   0.748    -.7429268    1.034546
            MELLEMLA  |   .1388564   .3693404     0.38   0.707    -.5850376    .8627503
            LANGE VI  |   .5032416   .3988402     1.26   0.207    -.2784709    1.284954
                      |
               1.lgov |   2.971477   .2592829    11.46   0.000     2.463292    3.479662
         1.lparty_pro |   -.034869   .2828032    -0.12   0.902    -.5891531    .5194151
                  age |   .0090787   .0076872     1.18   0.238    -.0059879    .0241453
               1.male |  -.6650504   .2034124    -3.27   0.001    -1.063731   -.2663694
                _cons |  -7.275493   .7996733    -9.10   0.000    -8.842824   -5.708162
---------------------------------------------------------------------------------------

. eststo b1: logit party_pro c.wave##(c.integration) c.eval c.econ c.ideology##c.ideology i.ny_udd i.lgov i.lparty_pro c.age i.male

Iteration 0:   log likelihood = -1030.0867  
Iteration 1:   log likelihood = -656.92622  
Iteration 2:   log likelihood = -643.43312  
Iteration 3:   log likelihood = -643.29131  
Iteration 4:   log likelihood = -643.29121  
Iteration 5:   log likelihood = -643.29121  

Logistic regression                               Number of obs   =       1534
                                                  LR chi2(17)     =     773.59
                                                  Prob > chi2     =     0.0000
Log likelihood = -643.29121                       Pseudo R2       =     0.3755

---------------------------------------------------------------------------------------
            party_pro |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
                 wave |  -.1062519   .0443595    -2.40   0.017    -.1931948   -.0193089
          integration |    .555145   .0641999     8.65   0.000     .4293155    .6809745
                      |
 c.wave#c.integration |    .016692    .009185     1.82   0.069    -.0013102    .0346943
                      |
                 eval |   .1753016   .0309836     5.66   0.000      .114575    .2360283
                 econ |   .0877842   .3673521     0.24   0.811    -.6322127    .8077811
             ideology |   .0675947   .0775033     0.87   0.383     -.084309    .2194985
                      |
c.ideology#c.ideology |  -.0122313   .0092189    -1.33   0.185    -.0303001    .0058374
                      |
               ny_udd |
            ALMENGYM  |  -.2444576   .3727394    -0.66   0.512    -.9750134    .4860982
            ERHVERVS  |  -.4718892   .4279491    -1.10   0.270    -1.310654    .3668757
            ERHVERVS  |   .1075098   .2537662     0.42   0.672    -.3898628    .6048825
            KORTE VI  |   .2056135   .3036043     0.68   0.498    -.3894399     .800667
            MELLEMLA  |   .0417147   .2443694     0.17   0.864    -.4372405    .5206699
            LANGE VI  |   .2047945   .2811724     0.73   0.466    -.3462932    .7558822
                      |
               1.lgov |   1.138873   .2096988     5.43   0.000     .7278706    1.549875
         1.lparty_pro |  -.2254476   .1650589    -1.37   0.172    -.5489572    .0980619
                  age |  -.0003047   .0054319    -0.06   0.955     -.010951    .0103416
               1.male |  -.8119824   .1501637    -5.41   0.000    -1.106298   -.5176669
                _cons |  -2.347602   .5065752    -4.63   0.000    -3.340471   -1.354733
---------------------------------------------------------------------------------------

. 
. 
. **Writing table 2*
. esttab a1 b1 using tabel2.rtf, replace stats(N r2_p ll, label("Observation" "Pseudo R squared" "Log likelihood") fmt(%9.0f %9.2f %9.0f) ) ///
> keep(ideology econ c.ideology#c.ideology 1.lgov 1.lparty_pro c.wave#c.integration integration eval c.wave#c.eval 1.male age wave 2.ny_udd 3.ny_udd 4.ny_udd 5.ny_udd 6.n
> y_udd 7.ny_udd) label ///
> varlabel(1.male "Male (ref: Female)" age "Age (years)" wave "Time in weeks" c.wave#c.integration "Weeks X Pro-integration" c.wave#c.integration "Weeks X Goverment"   //
> /
> integration "Pro-integration attitude" eval "Pro-government attitude" ///
> 2.ny_udd "High School (ref: Primary school)" 3.ny_udd "Vocational high school" 4.ny_udd "Vocational school" ///
> 5.ny_udd "Shorter tertiary education" 6.ny_udd "Tertiary education" 7.ny_udd "Longer tertiary education") ///
> star("*" 0.05) se b(%9.2f) varwidth(40) title("Table 2. The degree to which EU attitudes and national politics matter for voters") ///
> order(wave   eval integration c.wave#c.eval  c.wave#c.integration econ ideology c.ideology#c.ideology 2.ny_udd 3.ny_udd 4.ny_udd 5.ny_udd 6.ny_udd 7.ny_udd 1.lgov 1.lpa
> rty_pro age 1.male)
(note: file tabel2.rtf not found)
(output written to tabel2.rtf)

. 
. 
. *changing wave back to draw the figures
. replace wave=12-wave
(1942 real changes made)

. 
. 
. *Left panel of figure 6
. logit gov c.wave##(c.eval) c.econ c.integration c.ideology##c.ideology i.ny_udd i.lgov i.lparty_pro c.age i.male 

Iteration 0:   log likelihood = -869.79879  
Iteration 1:   log likelihood = -448.54347  
Iteration 2:   log likelihood = -384.22642  
Iteration 3:   log likelihood = -379.01583  
Iteration 4:   log likelihood = -378.97536  
Iteration 5:   log likelihood = -378.97534  

Logistic regression                               Number of obs   =       1469
                                                  LR chi2(17)     =     981.65
                                                  Prob > chi2     =     0.0000
Log likelihood = -378.97534                       Pseudo R2       =     0.5643

---------------------------------------------------------------------------------------
                  gov |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
                 wave |  -.1312654   .0595413    -2.20   0.027    -.2479642   -.0145667
                 eval |   .1457377   .0678829     2.15   0.032     .0126897    .2787857
                      |
        c.wave#c.eval |   .0309296   .0100346     3.08   0.002     .0112623     .050597
                      |
                 econ |   .1684713   .5151752     0.33   0.744    -.8412535    1.178196
          integration |   .3837783    .047504     8.08   0.000     .2906722    .4768844
             ideology |   .5113537    .115055     4.44   0.000       .28585    .7368573
                      |
c.ideology#c.ideology |  -.0978051   .0164821    -5.93   0.000    -.1301094   -.0655009
                      |
               ny_udd |
            ALMENGYM  |   1.234365   .5028599     2.45   0.014     .2487778    2.219952
            ERHVERVS  |   .2246049   .6054933     0.37   0.711    -.9621402     1.41135
            ERHVERVS  |   .3894889    .385262     1.01   0.312    -.3656108    1.144589
            KORTE VI  |   .1458098   .4534454     0.32   0.748    -.7429268    1.034546
            MELLEMLA  |   .1388564   .3693404     0.38   0.707    -.5850376    .8627503
            LANGE VI  |   .5032416   .3988402     1.26   0.207    -.2784709    1.284954
                      |
               1.lgov |   2.971477   .2592829    11.46   0.000     2.463292    3.479662
         1.lparty_pro |   -.034869   .2828032    -0.12   0.902    -.5891531    .5194151
                  age |   .0090787   .0076872     1.18   0.238    -.0059879    .0241453
               1.male |  -.6650504   .2034124    -3.27   0.001    -1.063731   -.2663694
                _cons |  -5.700308   .7812806    -7.30   0.000     -7.23159   -4.169026
---------------------------------------------------------------------------------------

. margins, dydx(eval) at(wave=(0(2)12)) post coeflegend

Average marginal effects                          Number of obs   =       1469
Model VCE    : OIM

Expression   : Pr(gov), predict()
dy/dx w.r.t. : eval

1._at        : wave            =           0

2._at        : wave            =           2

3._at        : wave            =           4

4._at        : wave            =           6

5._at        : wave            =           8

6._at        : wave            =          10

7._at        : wave            =          12

------------------------------------------------------------------------------
             |      dy/dx  Legend
-------------+----------------------------------------------------------------
eval         |
         _at |
          1  |   .0122024  _b[1bn._at]
          2  |   .0168911  _b[2._at]
          3  |    .021333  _b[3._at]
          4  |   .0255607  _b[4._at]
          5  |    .029602  _b[5._at]
          6  |   .0334772  _b[6._at]
          7  |    .037199  _b[7._at]
------------------------------------------------------------------------------

. marginsplot, xscale(reverse) ylabel(, labsize(large)) level(90) ///
>  graphregion(col(white)) scheme(s2mono) ytitle("Effect of pro-government attitude" "on voting for governing party" " ", size(large) ) ///
>  xtitle(" " "Weeks before the election", size(large)) ///
>  name(eval, replace) title( " ") ymtick(0(0.015)0.15) ylab(0.0(0.03)0.15, labsize(large))

  Variables that uniquely identify margins: wave

. 
.  *Wald test for equality of marginal effects - pro-gov; reported in paper
.  test _b[1bn._at]==_b[7._at]

 ( 1)  [eval]1bn._at - [eval]7._at = 0

           chi2(  1) =   10.59
         Prob > chi2 =    0.0011

. 
. *Right panel of figure 6
. logit party_pro c.wave##(c.integration) c.eval c.econ c.ideology##c.ideology i.ny_udd i.lgov i.lparty_pro c.age i.male

Iteration 0:   log likelihood = -1030.0867  
Iteration 1:   log likelihood = -656.92622  
Iteration 2:   log likelihood = -643.43312  
Iteration 3:   log likelihood = -643.29131  
Iteration 4:   log likelihood = -643.29121  
Iteration 5:   log likelihood = -643.29121  

Logistic regression                               Number of obs   =       1534
                                                  LR chi2(17)     =     773.59
                                                  Prob > chi2     =     0.0000
Log likelihood = -643.29121                       Pseudo R2       =     0.3755

---------------------------------------------------------------------------------------
            party_pro |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
                 wave |   .1062519   .0443595     2.40   0.017     .0193089    .1931948
          integration |   .7554494   .0722308    10.46   0.000     .6138796    .8970191
                      |
 c.wave#c.integration |   -.016692    .009185    -1.82   0.069    -.0346943    .0013102
                      |
                 eval |   .1753016   .0309836     5.66   0.000      .114575    .2360283
                 econ |   .0877842   .3673521     0.24   0.811    -.6322127    .8077811
             ideology |   .0675947   .0775033     0.87   0.383     -.084309    .2194985
                      |
c.ideology#c.ideology |  -.0122313   .0092189    -1.33   0.185    -.0303001    .0058374
                      |
               ny_udd |
            ALMENGYM  |  -.2444576   .3727394    -0.66   0.512    -.9750134    .4860982
            ERHVERVS  |  -.4718892   .4279491    -1.10   0.270    -1.310654    .3668757
            ERHVERVS  |   .1075098   .2537662     0.42   0.672    -.3898628    .6048825
            KORTE VI  |   .2056135   .3036043     0.68   0.498    -.3894399     .800667
            MELLEMLA  |   .0417147   .2443694     0.17   0.864    -.4372405    .5206699
            LANGE VI  |   .2047945   .2811724     0.73   0.466    -.3462932    .7558822
                      |
               1.lgov |   1.138873   .2096988     5.43   0.000     .7278706    1.549875
         1.lparty_pro |  -.2254476   .1650589    -1.37   0.172    -.5489572    .0980619
                  age |  -.0003047   .0054319    -0.06   0.955     -.010951    .0103416
               1.male |  -.8119824   .1501637    -5.41   0.000    -1.106298   -.5176669
                _cons |  -3.622624   .5498197    -6.59   0.000    -4.700251   -2.544998
---------------------------------------------------------------------------------------

. margins, dydx(integration) at(wave=(0(2)12)) post

Average marginal effects                          Number of obs   =       1534
Model VCE    : OIM

Expression   : Pr(party_pro), predict()
dy/dx w.r.t. : integration

1._at        : wave            =           0

2._at        : wave            =           2

3._at        : wave            =           4

4._at        : wave            =           6

5._at        : wave            =           8

6._at        : wave            =          10

7._at        : wave            =          12

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
integration  |
         _at |
          1  |   .0958236   .0045157    21.22   0.000     .0869729    .1046742
          2  |   .0935126   .0040051    23.35   0.000     .0856628    .1013624
          3  |   .0910423   .0035829    25.41   0.000     .0840199    .0980647
          4  |   .0883975   .0034023    25.98   0.000      .081729    .0950659
          5  |   .0855628   .0036571    23.40   0.000     .0783951    .0927305
          6  |   .0825231   .0044439    18.57   0.000     .0738132    .0912331
          7  |   .0792641   .0057171    13.86   0.000     .0680588    .0904694
------------------------------------------------------------------------------

. marginsplot, xscale(reverse) ylabel(, labsize(large)) level(90) ///
>  graphregion(col(white)) scheme(s2mono) ytitle("Effect of pro-integration attitudes" "on voting for pro-EU party" " ", size(large) ) ///
>  xtitle(" " "Weeks before the election", size(large)) ///
>  name(integrate, replace) title( " ") ymtick(0(0.015)0.15) ylab(0.0(0.03)0.15, labsize(large))

  Variables that uniquely identify margins: wave

. 
.   *Wald test for equality of marginal effects - pro-EU; reported in paper
.  test _b[1bn._at]==_b[7._at]

 ( 1)  [integration]1bn._at - [integration]7._at = 0

           chi2(  1) =    4.66
         Prob > chi2 =    0.0309

.   
.   
.   
. *Exporting figure 6
. graph combine eval integrate, xsize(16) ysize(7) scheme(s1mono) 

. graph export figure6.eps, replace
(note: file figure6.eps not found)
(file figure6.eps written in EPS format)

. 
. 
.  *****************************************
.  *ANALYSIS PART 3: Supplementary material*
.  *****************************************
. 
. *Figure S1
. *Placebo test for ideology - government
. logit gov c.wave##(c.ideology) c.integration c.eval c.econ  i.ny_udd i.lgov i.lparty_pro c.age i.male 

Iteration 0:   log likelihood = -869.79879  
Iteration 1:   log likelihood = -457.20498  
Iteration 2:   log likelihood =  -406.1379  
Iteration 3:   log likelihood = -403.93282  
Iteration 4:   log likelihood = -403.92903  
Iteration 5:   log likelihood = -403.92903  

Logistic regression                               Number of obs   =       1469
                                                  LR chi2(16)     =     931.74
                                                  Prob > chi2     =     0.0000
Log likelihood = -403.92903                       Pseudo R2       =     0.5356

-----------------------------------------------------------------------------------
              gov |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
             wave |   .0396591   .0371165     1.07   0.285     -.033088    .1124062
         ideology |  -.1391565   .0645424    -2.16   0.031    -.2656572   -.0126558
                  |
c.wave#c.ideology |  -.0019917   .0089576    -0.22   0.824    -.0195482    .0155649
                  |
      integration |    .364029   .0457691     7.95   0.000     .2743232    .4537348
             eval |   .3123398   .0395978     7.89   0.000     .2347296      .38995
             econ |   .3066331   .5031924     0.61   0.542    -.6796058    1.292872
                  |
           ny_udd |
        ALMENGYM  |   .9990556   .4893327     2.04   0.041     .0399811     1.95813
        ERHVERVS  |   .1349116   .5944552     0.23   0.820    -1.030199    1.300022
        ERHVERVS  |   .3850918   .3781151     1.02   0.308    -.3560003    1.126184
        KORTE VI  |   .2127554   .4397375     0.48   0.629    -.6491142    1.074625
        MELLEMLA  |   .1151602   .3615755     0.32   0.750    -.5935148    .8238352
        LANGE VI  |   .5348643   .3865967     1.38   0.167    -.2228513     1.29258
                  |
           1.lgov |   3.055043   .2520092    12.12   0.000     2.561114    3.548972
     1.lparty_pro |   .1755161   .2741264     0.64   0.522    -.3617617    .7127939
              age |   .0040702    .007365     0.55   0.581    -.0103649    .0185053
           1.male |  -.6046878    .196326    -3.08   0.002    -.9894796    -.219896
            _cons |  -6.046746   .7115728    -8.50   0.000    -7.441403   -4.652089
-----------------------------------------------------------------------------------

. margins, dydx(ideology) at(wave=(0(2)12))

Average marginal effects                          Number of obs   =       1469
Model VCE    : OIM

Expression   : Pr(gov), predict()
dy/dx w.r.t. : ideology

1._at        : wave            =           0

2._at        : wave            =           2

3._at        : wave            =           4

4._at        : wave            =           6

5._at        : wave            =           8

6._at        : wave            =          10

7._at        : wave            =          12

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ideology     |
         _at |
          1  |  -.0111829    .005004    -2.23   0.025    -.0209906   -.0013752
          2  |  -.0115884   .0039986    -2.90   0.004    -.0194255   -.0037512
          3  |   -.012001   .0032572    -3.68   0.000    -.0183851    -.005617
          4  |  -.0124211   .0030005    -4.14   0.000     -.018302   -.0065403
          5  |   -.012849   .0033574    -3.83   0.000    -.0194294   -.0062685
          6  |  -.0132847    .004186    -3.17   0.002    -.0214891   -.0050802
          7  |  -.0137285   .0052783    -2.60   0.009    -.0240737   -.0033833
------------------------------------------------------------------------------

. marginsplot, xscale(reverse) ylabel(, labsize(large)) level(90) ///
>  graphregion(col(white)) scheme(s2mono) ytitle("Effect of ideology (Governing parties)" " ", size(large) ) ///
>  xtitle(" " "Weeks before the election", size(large)) ///
>  name(ideo1, replace) title( " ")   ylab(-0.02(0.01)0.05, labsize(large))

  Variables that uniquely identify margins: wave

. 
. 
. *Placebo test for ideology - party_pro 
. logit party_pro c.wave##(c.ideology) c.eval c.integration c.econ i.ny_udd i.lgov i.lparty_pro c.age i.male

Iteration 0:   log likelihood = -1030.0867  
Iteration 1:   log likelihood = -658.28992  
Iteration 2:   log likelihood =  -645.9161  
Iteration 3:   log likelihood = -645.83771  
Iteration 4:   log likelihood = -645.83769  

Logistic regression                               Number of obs   =       1534
                                                  LR chi2(16)     =     768.50
                                                  Prob > chi2     =     0.0000
Log likelihood = -645.83769                       Pseudo R2       =     0.3730

-----------------------------------------------------------------------------------
        party_pro |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
             wave |   .0223731   .0335109     0.67   0.504    -.0433071    .0880532
         ideology |  -.0422112   .0455523    -0.93   0.354     -.131492    .0470697
                  |
c.wave#c.ideology |   .0022501   .0061991     0.36   0.717    -.0098999    .0144001
                  |
             eval |   .1800987   .0309351     5.82   0.000      .119467    .2407304
      integration |    .652432   .0400628    16.29   0.000     .5739104    .7309536
             econ |   .0772682   .3681361     0.21   0.834    -.6442654    .7988017
                  |
           ny_udd |
        ALMENGYM  |  -.2814816   .3741725    -0.75   0.452    -1.014846    .4518831
        ERHVERVS  |  -.4934736   .4280458    -1.15   0.249    -1.332428    .3454808
        ERHVERVS  |   .1037148   .2545394     0.41   0.684    -.3951733    .6026028
        KORTE VI  |   .2160095   .3032492     0.71   0.476     -.378348    .8103669
        MELLEMLA  |   .0357036   .2443798     0.15   0.884     -.443272    .5146793
        LANGE VI  |   .2046337   .2808337     0.73   0.466    -.3457902    .7550575
                  |
           1.lgov |   1.160337   .2079751     5.58   0.000     .7527134    1.567961
     1.lparty_pro |  -.1999263   .1646554    -1.21   0.225    -.5226449    .1227923
              age |  -.0012489   .0054316    -0.23   0.818    -.0118947    .0093969
           1.male |  -.8055901   .1498767    -5.38   0.000    -1.099343   -.5118373
            _cons |  -2.994588   .5025913    -5.96   0.000    -3.979649   -2.009527
-----------------------------------------------------------------------------------

. margins, dydx(ideology) at(wave=(0(2)12))

Average marginal effects                          Number of obs   =       1534
Model VCE    : OIM

Expression   : Pr(party_pro), predict()
dy/dx w.r.t. : ideology

1._at        : wave            =           0

2._at        : wave            =           2

3._at        : wave            =           4

4._at        : wave            =           6

5._at        : wave            =           8

6._at        : wave            =          10

7._at        : wave            =          12

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ideology     |
         _at |
          1  |  -.0057906   .0062243    -0.93   0.352      -.01799    .0064088
          2  |  -.0051529   .0049403    -1.04   0.297    -.0148358      .00453
          3  |  -.0045176   .0039851    -1.13   0.257    -.0123282     .003293
          4  |  -.0038857   .0036159    -1.07   0.283    -.0109727    .0032014
          5  |  -.0032582   .0039818    -0.82   0.413    -.0110623     .004546
          6  |  -.0026361   .0049034    -0.54   0.591    -.0122466    .0069744
          7  |  -.0020205   .0061174    -0.33   0.741    -.0140104    .0099694
------------------------------------------------------------------------------

. marginsplot, xscale(reverse) ylabel(, labsize(large)) level(90) ///
>  graphregion(col(white)) scheme(s2mono) ytitle("Effect of ideology (pro-EU)" " ", size(large) ) ///
>  xtitle(" " "Weeks before the election", size(large)) ///
>  name(ideo2, replace) title( " ") ylab(-0.02(0.01)0.05, labsize(large))

  Variables that uniquely identify margins: wave

. 
. *Exporting figure S1
. graph combine ideo1 ideo2, xsize(16) ysize(7) scheme(s1mono) 

. graph export s1fig.eps, replace
(note: file s1fig.eps not found)
(file s1fig.eps written in EPS format)

.  
.  
. *Figure S2
. *Alternative independent variable 
. logit gov c.wave##(c.econ)  c.ideology##c.ideology  c.integration i.ny_udd i.lgov i.lparty_pro c.age i.male

Iteration 0:   log likelihood = -869.79879  
Iteration 1:   log likelihood = -474.22333  
Iteration 2:   log likelihood = -420.36392  
Iteration 3:   log likelihood = -416.45512  
Iteration 4:   log likelihood = -416.44119  
Iteration 5:   log likelihood = -416.44119  

Logistic regression                               Number of obs   =       1469
                                                  LR chi2(16)     =     906.72
                                                  Prob > chi2     =     0.0000
Log likelihood = -416.44119                       Pseudo R2       =     0.5212

---------------------------------------------------------------------------------------
                  gov |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
                 wave |  -.0955012   .0793367    -1.20   0.229    -.2509982    .0599958
                 econ |   .3776595   .8552052     0.44   0.659    -1.298512    2.053831
                      |
        c.wave#c.econ |   .1695455   .1226167     1.38   0.167    -.0707788    .4098697
                      |
             ideology |   .4374621    .110061     3.97   0.000     .2217465    .6531776
                      |
c.ideology#c.ideology |  -.0943448   .0160701    -5.87   0.000    -.1258416    -.062848
                      |
          integration |   .4365261   .0440673     9.91   0.000     .3501558    .5228964
                      |
               ny_udd |
            ALMENGYM  |   1.150437    .479348     2.40   0.016      .210932    2.089942
            ERHVERVS  |   .2649884   .5862711     0.45   0.651    -.8840819    1.414059
            ERHVERVS  |      .4411   .3589229     1.23   0.219     -.262376    1.144576
            KORTE VI  |   .2489504   .4216482     0.59   0.555     -.577465    1.075366
            MELLEMLA  |   .0878927   .3437497     0.26   0.798    -.5858443    .7616297
            LANGE VI  |   .4440279   .3721874     1.19   0.233     -.285446    1.173502
                      |
               1.lgov |   3.050476   .2483637    12.28   0.000     2.563692     3.53726
         1.lparty_pro |  -.0126309   .2717938    -0.05   0.963    -.5453369    .5200751
                  age |   .0139526   .0072875     1.91   0.056    -.0003306    .0282359
               1.male |  -.6587147   .1923204    -3.43   0.001    -1.035656   -.2817735
                _cons |   -5.40242   .8056661    -6.71   0.000    -6.981496   -3.823343
---------------------------------------------------------------------------------------

. margins, dydx(econ) at(wave=(0(2)12)) post

Average marginal effects                          Number of obs   =       1469
Model VCE    : OIM

Expression   : Pr(gov), predict()
dy/dx w.r.t. : econ

1._at        : wave            =           0

2._at        : wave            =           2

3._at        : wave            =           4

4._at        : wave            =           6

5._at        : wave            =           8

6._at        : wave            =          10

7._at        : wave            =          12

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
econ         |
         _at |
          1  |   .0329437    .074102     0.44   0.657    -.1122936     .178181
          2  |   .0621484   .0570308     1.09   0.276    -.0496299    .1739267
          3  |   .0910125   .0445058     2.04   0.041     .0037828    .1782422
          4  |   .1195493   .0400047     2.99   0.003     .0411415    .1979571
          5  |   .1477726   .0452008     3.27   0.001     .0591806    .2363646
          6  |   .1756974   .0569906     3.08   0.002     .0639978    .2873969
          7  |   .2033396    .071842     2.83   0.005     .0625318    .3441474
------------------------------------------------------------------------------

. marginsplot, xscale(reverse) ylabel(, labsize(large)) level(90) ///
>  graphregion(col(white)) scheme(s2mono) yline(0) ytitle("Effect of economic evaluations" "on voting for governing party" " ", size(large) ) ///
>  xtitle(" " "Weeks before the election", size(large)) ///
> title( " ") ylab(-0.1(0.1)0.3, labsize(large))

  Variables that uniquely identify margins: wave

. 
.  *Exporting figure S2
. graph export s2fig.eps, replace
(note: file s2fig.eps not found)
(file s2fig.eps written in EPS format)

.  
. *Wald test for equality of marginal effects
. test _b[1bn._at]==_b[2._at]==_b[3._at]==_b[4._at]==_b[5._at]==_b[6._at]==_b[7._at]

 ( 1)  [econ]1bn._at - [econ]2._at = 0
 ( 2)  [econ]1bn._at - [econ]3._at = 0
 ( 3)  [econ]1bn._at - [econ]4._at = 0
 ( 4)  [econ]1bn._at - [econ]5._at = 0
 ( 5)  [econ]1bn._at - [econ]6._at = 0
 ( 6)  [econ]1bn._at - [econ]7._at = 0
       Constraint 2 dropped
       Constraint 4 dropped
       Constraint 5 dropped

           chi2(  3) =   34.17
         Prob > chi2 =    0.0000

. 
.   
. *Table of descriptive statistics (table S1)
. 
. *Generating  dummy variables
.  tab ny_udd, gen(udd)

Educational |
 attainment |      Freq.     Percent        Cum.
------------+-----------------------------------
   GRUNDSKO |        254       11.00       11.00
   ALMENGYM |        164        7.10       18.10
   ERHVERVS |         83        3.59       21.69
   ERHVERVS |        497       21.52       43.20
   KORTE VI |        243       10.52       53.72
   MELLEMLA |        715       30.95       84.68
   LANGE VI |        354       15.32      100.00
------------+-----------------------------------
      Total |      2,310      100.00

.  ta wave, gen(waves)

Weeks until |
   election |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        318       13.77       13.77
          2 |        324       14.03       27.79
          4 |        315       13.64       41.43
          6 |        368       15.93       57.36
          8 |        359       15.54       72.90
         10 |        297       12.86       85.76
         12 |        329       14.24      100.00
------------+-----------------------------------
      Total |      2,310      100.00

.  
.  la var udd1 "Primary education"

.  la var udd2 "Secondary school"

.  la var udd3 "Vocational secondary school"

.  la var udd4 "Vocational school"

.  la var udd5 "Short tertiary education"

.  la var udd6 "Long tertiary education"

.  la var udd7 "Longer tertiary education"

.  
.  la var waves1 "0 weeks out"

.  la var waves2 "2 weeks out"

.  la var waves3 "4 weeks out"

.  la var waves4 "6 weeks out"

.  la var waves5 "8 weeks out"

.  la var waves6 "10 weeks out"

.  la var waves7 "12 weeks out"

. 
. 
.  
. *Writing table
. file open anyname using tabels1.txt, write text replace
(note: file tabels1.txt not found)

. file write anyname  _newline  _col(5)  "Table S1: Descriptive statistics" _newline

. file write anyname  _newline  _col(5) "Variable" _col(80) "MEAN" _col(90) "STD DEV" _col(100) "MIN" _col(110) "MAX" _col(120) "N"  _newline

.  foreach x of varlist eval int_dk int_eu dk patent_cor integration party_pro gov lparty_pro lgov ideology econ male age  udd* waves* {
  2.  su `x', d
  3.  file write anyname  _col(5) `"`: var label `x''"'  _col(80) %9.2f (r(mean)) _col(90) %9.2f (r(sd)) _col(100) %9.2f (r(min)) _col(110) %9.2f  (r(max)) _col(120) (r(N
> ))  _newline
  4.  }

                  Pro-government attitudes
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs                2310
25%          2.5              0       Sum of Wgt.        2310

50%            5                      Mean           4.151515
                        Largest       Std. Dev.      2.661986
75%            5             10
90%          7.5             10       Variance       7.086171
95%          7.5             10       Skewness       .1822147
99%           10             10       Kurtosis       2.363915

                    Interest in politics
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%           25              0
10%           25              0       Obs                2310
25%           75              0       Sum of Wgt.        2310

50%           75                      Mean           75.29221
                        Largest       Std. Dev.      23.85696
75%          100            100
90%          100            100       Variance       569.1545
95%          100            100       Skewness      -1.222631
99%          100            100       Kurtosis       4.361578

                   Interest in EU politics
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%           25              0
10%           25              0       Obs                2310
25%           50              0       Sum of Wgt.        2310

50%           75                      Mean           63.58225
                        Largest       Std. Dev.      26.04034
75%           75            100
90%          100            100       Variance       678.0996
95%          100            100       Skewness      -.7522586
99%          100            100       Kurtosis       2.864377

                          Informed
-------------------------------------------------------------
      Percentiles      Smallest
 1%           40              0
 5%           60              0
10%           60              0       Obs                2310
25%          100              0       Sum of Wgt.        2310

50%          100                      Mean           90.50216
                        Largest       Std. Dev.      18.78069
75%          100            100
90%          100            100       Variance       352.7144
95%          100            100       Skewness      -1.974835
99%          100            100       Kurtosis       6.574136

                   Knowledge: Patent court
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs                1706
25%     66.66666              0       Sum of Wgt.        1706

50%          100                      Mean           75.53732
                        Largest       Std. Dev.       36.2208
75%          100            100
90%          100            100       Variance       1311.946
95%          100            100       Skewness      -1.230672
99%          100            100       Kurtosis       3.008382

                  Pro-integration attitudes
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%         1.25              0       Obs                2181
25%          2.5              0       Sum of Wgt.        2181

50%            5                      Mean            4.68707
                        Largest       Std. Dev.      2.431886
75%         6.25             10
90%          7.5             10       Variance       5.914071
95%         8.75             10       Skewness       .0031911
99%           10             10       Kurtosis       2.466736

                    Integrationist voters
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs                1642
25%            0              0       Sum of Wgt.        1642

50%            1                      Mean           .6023143
                        Largest       Std. Dev.       .489569
75%            1              1
90%            1              1       Variance       .2396778
95%            1              1       Skewness      -.4181042
99%            1              1       Kurtosis       1.174811

                      Government voters
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs                1573
25%            0              0       Sum of Wgt.        1573

50%            0                      Mean           .2784488
                        Largest       Std. Dev.      .4483781
75%            1              1
90%            1              1       Variance       .2010429
95%            1              1       Skewness       .9885481
99%            1              1       Kurtosis       1.977227

      Voted for Pro-EU party at last national election
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs                2310
25%            0              0       Sum of Wgt.        2310

50%            1                      Mean           .5372294
                        Largest       Std. Dev.        .49872
75%            1              1
90%            1              1       Variance       .2487216
95%            1              1       Skewness      -.1493323
99%            1              1       Kurtosis         1.0223

         Voted for government party at last national
                          election
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs                2310
25%            0              0       Sum of Wgt.        2310

50%            0                      Mean           .3073593
                        Largest       Std. Dev.      .4614995
75%            1              1
90%            1              1       Variance       .2129818
95%            1              1       Skewness       .8350276
99%            1              1       Kurtosis       1.697271

                          Ideology
-------------------------------------------------------------
      Percentiles      Smallest
 1%           -1             -1
 5%           -1             -1
10%            0             -1       Obs                2173
25%            2             -1       Sum of Wgt.        2173

50%            4                      Mean           4.029452
                        Largest       Std. Dev.      2.788436
75%            6              9
90%            7              9       Variance       7.775375
95%            9              9       Skewness      -.0541676
99%            9              9       Kurtosis       1.978701

                National Economic Perceptions
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%          .25              0
10%          .25              0       Obs                2212
25%           .5              0       Sum of Wgt.        2212

50%           .5                      Mean           .5502939
                        Largest       Std. Dev.      .2091499
75%          .75              1
90%          .75              1       Variance       .0437437
95%          .75              1       Skewness      -.6327252
99%            1              1       Kurtosis       2.967534

                     Male (ref: Female)
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs                2310
25%            0              0       Sum of Wgt.        2310

50%            0                      Mean           .4757576
                        Largest       Std. Dev.      .4995201
75%            1              1
90%            1              1       Variance       .2495203
95%            1              1       Skewness       .0970839
99%            1              1       Kurtosis       1.009425

                        Age in years
-------------------------------------------------------------
      Percentiles      Smallest
 1%           20             19
 5%           24             19
10%           28             19       Obs                2310
25%           43             19       Sum of Wgt.        2310

50%           54                      Mean           51.58052
                        Largest       Std. Dev.      14.61515
75%           63             80
90%           69             80       Variance       213.6027
95%           72             80       Skewness      -.4727542
99%           76             93       Kurtosis       2.405611

                      Primary education
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs                2310
25%            0              0       Sum of Wgt.        2310

50%            0                      Mean           .1099567
                        Largest       Std. Dev.      .3129035
75%            0              1
90%            1              1       Variance       .0979086
95%            1              1       Skewness       2.493598
99%            1              1       Kurtosis       7.218029

                      Secondary school
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs                2310
25%            0              0       Sum of Wgt.        2310

50%            0                      Mean           .0709957
                        Largest       Std. Dev.      .2568732
75%            0              1
90%            0              1       Variance       .0659839
95%            1              1       Skewness       3.340926
99%            1              1       Kurtosis       12.16179

                 Vocational secondary school
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs                2310
25%            0              0       Sum of Wgt.        2310

50%            0                      Mean           .0359307
                        Largest       Std. Dev.      .1861578
75%            0              1
90%            0              1       Variance       .0346547
95%            0              1       Skewness       4.986842
99%            1              1       Kurtosis        25.8686

                      Vocational school
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs                2310
25%            0              0       Sum of Wgt.        2310

50%            0                      Mean           .2151515
                        Largest       Std. Dev.      .4110164
75%            0              1
90%            1              1       Variance       .1689345
95%            1              1       Skewness       1.386369
99%            1              1       Kurtosis       2.922019

                  Short tertiary education
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs                2310
25%            0              0       Sum of Wgt.        2310

50%            0                      Mean           .1051948
                        Largest       Std. Dev.      .3068707
75%            0              1
90%            1              1       Variance       .0941696
95%            1              1       Skewness       2.573662
99%            1              1       Kurtosis       7.623735

                   Long tertiary education
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs                2310
25%            0              0       Sum of Wgt.        2310

50%            0                      Mean           .3095238
                        Largest       Std. Dev.      .4623974
75%            1              1
90%            1              1       Variance       .2138114
95%            1              1       Skewness       .8240419
99%            1              1       Kurtosis       1.679045

                  Longer tertiary education
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs                2310
25%            0              0       Sum of Wgt.        2310

50%            0                      Mean           .1532468
                        Largest       Std. Dev.      .3603032
75%            0              1
90%            1              1       Variance       .1298184
95%            1              1       Skewness       1.925203
99%            1              1       Kurtosis       4.706405

                         0 weeks out
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs                2310
25%            0              0       Sum of Wgt.        2310

50%            0                      Mean           .1376623
                        Largest       Std. Dev.      .3446198
75%            0              1
90%            1              1       Variance       .1187628
95%            1              1       Skewness       2.103281
99%            1              1       Kurtosis       5.423789

                         2 weeks out
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs                2310
25%            0              0       Sum of Wgt.        2310

50%            0                      Mean           .1402597
                        Largest       Std. Dev.      .3473315
75%            0              1
90%            1              1       Variance       .1206392
95%            1              1       Skewness         2.0719
99%            1              1       Kurtosis       5.292772

                         4 weeks out
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs                2310
25%            0              0       Sum of Wgt.        2310

50%            0                      Mean           .1363636
                        Largest       Std. Dev.      .3432486
75%            0              1
90%            1              1       Variance       .1178196
95%            1              1       Skewness       2.119252
99%            1              1       Kurtosis       5.491228

                         6 weeks out
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs                2310
25%            0              0       Sum of Wgt.        2310

50%            0                      Mean           .1593074
                        Largest       Std. Dev.      .3660417
75%            0              1
90%            1              1       Variance       .1339865
95%            1              1       Skewness       1.861899
99%            1              1       Kurtosis       4.466669

                         8 weeks out
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs                2310
25%            0              0       Sum of Wgt.        2310

50%            0                      Mean           .1554113
                        Largest       Std. Dev.      .3623747
75%            0              1
90%            1              1       Variance       .1313154
95%            1              1       Skewness       1.902248
99%            1              1       Kurtosis       4.618549

                        10 weeks out
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs                2310
25%            0              0       Sum of Wgt.        2310

50%            0                      Mean           .1285714
                        Largest       Std. Dev.      .3347975
75%            0              1
90%            1              1       Variance       .1120893
95%            1              1       Skewness       2.219306
99%            1              1       Kurtosis       5.925319

                        12 weeks out
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs                2310
25%            0              0       Sum of Wgt.        2310

50%            0                      Mean           .1424242
                        Largest       Std. Dev.      .3495604
75%            0              1
90%            1              1       Variance       .1221925
95%            1              1       Skewness       2.046303
99%            1              1       Kurtosis       5.187354

.  file close anyname

. 
.  *Representativity (table S2, row 2) 
.  
.  *recoding education variable to match up with data from tatistics Denmark.
.  recode ny_udd (1=1 "Elementary") (2 3=2 "Lower Secondary") (4=4 "Vocational") (5=5 "Short tertiary") (6=6 "Medium tertiary") (7=7 "Longer tertiary") , gen(recoded_udd)
(83 differences between ny_udd and recoded_udd)

.  
. *defining labels for representativty variables 
. label define region  1 "Capital"  2 "Sealand"  3 "Southern Denmark" 4 "Mid-Jutland" 5 "North-Jutland"

. 
. label values region region

. 
. label define agegrp 1 "18-39" 2 "40-59" 3 "60+"

. 
. label values agegrp agegrp region

. 
.  
.  *numbers to match with table S2
.  ta region

    Geographical |
          region |      Freq.     Percent        Cum.
-----------------+-----------------------------------
         Capital |        757       32.77       32.77
         Sealand |        335       14.50       47.27
Southern Denmark |        456       19.74       67.01
     Mid-Jutland |        494       21.39       88.40
   North-Jutland |        268       11.60      100.00
-----------------+-----------------------------------
           Total |      2,310      100.00

.  ta male

 Male (ref: |
    Female) |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,211       52.42       52.42
          1 |      1,099       47.58      100.00
------------+-----------------------------------
      Total |      2,310      100.00

.  ta agegrp

   RECODE of age |      Freq.     Percent        Cum.
-----------------+-----------------------------------
         Capital |        501       21.69       21.69
         Sealand |      1,062       45.97       67.66
Southern Denmark |        747       32.34      100.00
-----------------+-----------------------------------
           Total |      2,310      100.00

.  ta recoded_udd

      RECODE of |
         ny_udd |
   (Educational |
    attainment) |      Freq.     Percent        Cum.
----------------+-----------------------------------
     Elementary |        254       11.00       11.00
Lower Secondary |        247       10.69       21.69
     Vocational |        497       21.52       43.20
 Short tertiary |        243       10.52       53.72
Medium tertiary |        715       30.95       84.68
Longer tertiary |        354       15.32      100.00
----------------+-----------------------------------
          Total |      2,310      100.00

.  
.  log close      
      name:  <unnamed>
       log:  C:\Users\mvl\Downloads\dataverse_files\survey_results.log
  log type:  text
 closed on:  24 Jan 2017, 08:51:20
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------
